R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 5391 articles , 6 standard articles )

Showing results 1 to 20 of 5391.
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  1. Tanaka, Kentaro: Conditional independence and linear programming (to appear) (2019)
  2. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  3. Aghamohammadi, Ali: Bayesian analysis of dynamic panel data by penalized quantile regression (2018)
  4. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  5. Álvarez de Toledo, Pablo; Núñez, Fernando; Usabiaga, Carlos: Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market (2018)
  6. Andrés Villegas; Vladimir Kaishev; Pietro Millossovich: StMoMo: An R Package for Stochastic Mortality Modeling (2018)
  7. Angelo Mazza; Antonio Punzo; Salvatore Ingrassia: flexCWM: A Flexible Framework for Cluster-Weighted Models (2018)
  8. Arbia, Giuseppe; Bee, Marco; Espa, Giuseppe; Santi, Flavio: Fitting spatial regressions to large datasets using unilateral approximations (2018)
  9. Ardia, David; Bluteau, Keven; Hoogerheide, Lennart F.: Methods for computing numerical standard errors: review and application to value-at-risk estimation (2018)
  10. Arellano-Valle, Reinaldo B.; Ferreira, Clécio S.; Genton, Marc G.: Scale and shape mixtures of multivariate skew-normal distributions (2018)
  11. Arens, Tilo; Hettlich, Frank; Karpfinger, Christian; Kockelkorn, Ulrich; Lichtenegger, Klaus; Stachel, Hellmuth: Mathematics (to appear) (2018)
  12. Augustyniak, Maciej; Boudreault, Mathieu; Morales, Manuel: Maximum likelihood estimation of the Markov-switching GARCH model based on a general collapsing procedure (2018)
  13. Baeumer, Boris; Kovács, Mihály; Meerschaert, Mark M.; Sankaranarayanan, Harish: Boundary conditions for fractional diffusion (2018)
  14. Baeumer, Boris; Kovács, Mihály; Meerschaert, Mark M.; Sankaranarayanan, Harish: Reprint of: boundary conditions for fractional diffusion (2018)
  15. Bai, Ray; Ghosh, Malay: High-dimensional multivariate posterior consistency under global-local shrinkage priors (2018)
  16. Balabdaoui, Fadoua; Doss, Charles R.: Inference for a two-component mixture of symmetric distributions under log-concavity (2018)
  17. Barigozzi, Matteo; Cho, Haeran; Fryzlewicz, Piotr: Simultaneous multiple change-point and factor analysis for high-dimensional time series (2018)
  18. Barroso Da Silva, Eveliny; Ribeiro Diniz, Carlos Alberto; Farfan Carrasco, Jalmar Manuel; De Castro, Mário: A class of beta regression models with multiplicative log-normal measurement errors (2018)
  19. Bartolucci, Francesco; Marino, Maria Francesca; Pandolfi, Silvia: Dealing with reciprocity in dynamic stochastic block models (2018)
  20. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)

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Further publications can be found at: http://journal.r-project.org/